The Next Step Beyond LLMs: Foundation Models for Inference

<p>Today's clip is from episode <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/amortized-inference-neural-processes-foundation-models-luigi-acerbi" target="_blank">161</a>, featuring Luigi Acerbi. In this conversation, Luigi explains one of the biggest engineering bottlenecks facing transformer-based probabilistic models—and how his group found a way around it.<br /><br />The core challenge is that many inference models treat data as an unordered set, making them naturally permutation invariant. That's statistically elegant, but computationally painful: every time a new data point arrives, the model has to recompute attention over the entire dataset from scratch, preventing the kind of KV caching that makes modern language models so efficient.<br /></p><p>Luigi walks through his team's solution: a hybrid architecture that keeps the original context fully set-based while introducing a causal-attention buffer for newly arriving data. The result is dramatically faster inference- up to 100× faster in some settings - opening the door to applications like reinforcement learning, active data acquisition, and, ultimately, Luigi's long-term vision of a foundation model for Bayesian inference.</p><p></p><p>Get the full discussion <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/amortized-inference-neural-processes-foundation-models-luigi-acerbi" target="_blank">here</a></p><p></p><p>Support &amp; Resources<br />→ Support the show on <a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank">Patreon</a><br />→ <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank">Bayesian Modeling Course</a> (first 2 lessons free)</p><p><br />Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his <a rel="noopener noreferrer nofollow" href="https://bababrinkman.com/" target="_blank">awesome work</a></p>